Loading...
Please wait, while we are loading the content...
Similar Documents
Machine Learning to Predict Area Fugitive Emission Fluxes of GHGs from Open-Pit Mines
| Content Provider | MDPI |
|---|---|
| Author | Jesse, Thé Kia, Seyed Ahmad Nambiar, Manoj K. Gharabaghi, Bahram Aliabadi, Amir A. |
| Copyright Year | 2022 |
| Description | Greenhouse gas (GHG) emissions from open-pit mines pose a global climate challenge, which necessitates appropriate quantification to support effective mitigation measures. This study considers the area-fugitive methane advective flux (as a proxy for emission flux) released from a tailings pond and two open-pit mines, denominated “old” and “new”, within a facility in northern Canada. To estimate the emission fluxes of methane from these sources, this research employed near-surface observations and modeling using the weather research and forecasting (WRF) passive tracer dispersion method. Various machine learning (ML) methods were trained and tested on these data for the operational forecasting of emissions. Predicted emission fluxes and meteorological variables from the WRF model were used as training and input datasets for ML algorithms. A series of 10 ML algorithms were evaluated. The four models that generated the most accurate forecasts were selected. These ML models are the multi-layer perception (MLP) artificial neural network, the gradient boosting (GBR), XGBOOST (XGB), and support vector machines (SVM). Overall, the simulations predicted the emission fluxes with R |
| Starting Page | 210 |
| e-ISSN | 20734433 |
| DOI | 10.3390/atmos13020210 |
| Journal | Atmosphere |
| Issue Number | 2 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-01-27 |
| Access Restriction | Open |
| Subject Keyword | Atmosphere Remote Sensing Emission Flux Machine Learning (ml) Method Open-pit Mines Weather Research and Forecasting (wrf) |
| Content Type | Text |
| Resource Type | Article |
